Top 10 Programming Languages For Data Scientists to Learn In 2018

Data science, also known as data-driven science, is an interdisciplinary field of scientific methods, processes, and systems to extract knowledge or insights from data in various forms, either structured or unstructured,similar to data mining.

Data science is a “concept to unify statistics, data analysis and their related methods” in order to “understand and analyze actual phenomena” with data.[3] It employs techniques and theories drawn from many fields within the broad areas of mathematicsstatisticsinformation science, and computer science, in particular from the subdomains of machine learningclassificationcluster analysisdata miningdatabases, and visualization.

Turing award winner Jim Gray imagined data science as a “fourth paradigm” of science (empiricaltheoretical, computational and now data-driven) and asserted that “everything about science is changing because of the impact of information technology” and the data deluge.

When Harvard Business Review called it “The Sexiest Job of the 21st Century”the term became a buzzword, and is now often applied to business analytics, or even arbitrary use of data, or used as a sexed-up term for statistics.While many university programs now offer a data science degree, there exists no consensus on a definition or curriculum contents.] Because of the current popularity of this term, there are many “advocacy efforts” surrounding it.

1. Python:

This language traces back to 1989, and is loved by its fans for its highly readable code. It has simple easy-to-use syntax, making it the perfect language for anybody trying to learn programming for the first time.

2. java

very now and then, a lot of new programming languages get introduced. But even after 22 years, Java has not lost its popularity.

One of the reasons why Java is so popular is because it is platform independent. What that means is that it can run on almost any of device. Android applications also use Java.

3. R:

R programming language is mostly used by data scientists to extract or mine data information from a large data sets. Google has gone on record as a big fan of R, for the power it gives to its mathematicians.

 

4. Julia:

Julia is a high-level dynamic programming language designed to address the needs of high-performance numerical analysis and computational science, without the typical need of separate compilation to be fast, while also being effective for general-purpose programming, web use or as a specification language.

Distinctive aspects of Julia’s design include a type system with parametric polymorphism and types in a fully dynamic programming language and multiple dispatch as its core programming paradigm. It allows concurrentparallel and distributed computing, and direct calling of C and Fortran libraries without glue code.

Julia is garbage-collected, uses eager evaluation and includes efficient libraries for floating-point calculations, linear algebrarandom number generationfast Fourier transforms and regular expression matching.

5. SAS:

SAS (previously “Statistical Analysis System”) is a software suite developed by SAS Institute for advanced analytics, multivariate analysesbusiness intelligencedata management, and predictive analytics.

SAS was developed at North Carolina State University from 1966 until 1976, when SAS Institute was incorporated. SAS was further developed in the 1980s and 1990s with the addition of new statistical procedures, additional components and the introduction of JMP. A point-and-click interface was added in version 9 in 2004. A social media analytics product was added in 2010.

6. SQL:

SQL ( Structured Query Language) is a domain-specific language used in programming and designed for managing data held in a relational database management system (RDBMS), or for stream processing in a relational data stream management system (RDSMS). In comparison to older read/write APIs like ISAM or VSAM, SQL offers two main advantages: first, it introduced the concept of accessing many records with one single command; and second, it eliminates the need to specify how to reach a record, e.g. with or without an index.

Originally based upon relational algebra and tuple relational calculus, SQL consists of many types of statements, which may be informally classed as sublanguages, commonly: a data query language (DQL), a data definition language (DDL),data control language (DCL), and a data manipulation language (DML) The scope of SQL includes data query, data manipulation (insert, update and delete), data definition (schema creation and modification), and data access control. Although SQL is often described as, and to a great extent is, a declarative language (4GL), it also includes procedural elements.

SQL was one of the first commercial languages for Edgar F. Codd‘s relational model, as described in his influential 1970 paper, “A Relational Model of Data for Large Shared Data Banks”.Despite not entirely adhering to the relational model as described by Codd, it became the most widely used database language.

SQL became a standard of the American National Standards Institute (ANSI) in 1986, and of the International Organization for Standardization (ISO) in 1987. Since then, the standard has been revised to include a larger set of features. Despite the existence of such standards, most SQL code is not completely portable among different database systems without adjustments.

7. MATLAB:

MATLAB (matrix laboratory) is a multi-paradigm numerical computing environment. A proprietary programming languagedeveloped by MathWorks, MATLAB allows matrix manipulations, plotting of functions and data, implementation of algorithms, creation of user interfaces, and interfacing with programs written in other languages, including CC++C#JavaFortran and Python.

Although MATLAB is intended primarily for numerical computing, an optional toolbox uses the MuPAD symbolic engine, allowing access to symbolic computing abilities. An additional package, Simulink, adds graphical multi-domain simulation and model-based design for dynamic and embedded systems.

As of 2017, MATLAB has roughly 1 million users across industry and academia. MATLAB users come from various backgrounds of engineeringscience, and economics.

8. Scala:

9. C:

R programming language is mostly used by data scientists to extract or mine data information from a large data sets. Google has gone on record as a big fan of R, for the power it gives to its mathematicians.

10. F#:

F# (pronounced F sharp) is a strongly typedmulti-paradigm programming language that encompasses functionalimperative, and object-oriented programming methods. F# is most often used as a cross-platform Common Language Infrastructure (CLI) language, but it can also generate JavaScript and graphics processing unit (GPU) code.

F# is developed by the F# Software Foundation, Microsoft and open contributors. An open source, cross-platform compiler for F# is available from the F# Software Foundation.F# is also a fully supported language in Visual Studio and Xamarin Studio. Other tools supporting F# development include MonoMonoDevelopSharpDevelopMBrace and WebSharper. Plug-ins supporting F# exist for many widely used editors, most notably the Ionide extension for Atom and Visual Studio Code, and integrations for other editors such as VimEmacs, and Sublime Text.

F# is member of the ML language family and originated as a .NET Framework implementation of a core of the programming language OCaml,[3][4] It has also been influenced by C#PythonHaskell,[2] Scala, and Erlang.

 

 

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